In [1]:
import ensembles as en
import pandas as pd
import numpy as np
import xgboost as xgb
import category_encoders as ce
from sklearn import datasets, linear_model, preprocessing, grid_search
from sklearn.preprocessing import Imputer, PolynomialFeatures, StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedKFold, KFold
from sklearn.preprocessing import OneHotEncoder
from sklearn.externals import joblib
from keras.layers import Dense, Activation, Dropout
from keras.models import Sequential
from keras.regularizers import l2, activity_l2
from sklearn.metrics import roc_auc_score, average_precision_score, f1_score, log_loss, accuracy_score, \
mean_absolute_error, mean_squared_error, r2_score
from sklearn.cross_validation import train_test_split
from joblib import Parallel, delayed
from sklearn.pipeline import Pipeline
from hyperopt import hp, fmin, tpe, STATUS_OK, Trials
from hyperas import optim
from hyperas.distributions import choice, uniform, conditional
from functools import partial
np.random.seed(1338)
In [2]:
#Setting number of layers in Multi Layer Perceptronset_no_of_layers
In [3]:
#3 Layers
en.set_no_of_layers(3)
In [4]:
#4 Layers
en.set_no_of_layers(4)
In [5]:
#5 Layers
en.set_no_of_layers(5)